Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations43818
Missing cells21915
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory176.0 B

Variable types

Categorical5
Text9
Numeric8

Alerts

Monthly_Inhand_Salary has 6548 (14.9%) missing valuesMissing
Type_of_Loan has 4946 (11.3%) missing valuesMissing
Num_of_Delayed_Payment has 3085 (7.0%) missing valuesMissing
Num_Credit_Inquiries has 840 (1.9%) missing valuesMissing
Credit_History_Age has 3986 (9.1%) missing valuesMissing
Amount_invested_monthly has 1940 (4.4%) missing valuesMissing
Monthly_Balance has 563 (1.3%) missing valuesMissing
Num_Bank_Accounts has 2005 (4.6%) zerosZeros
Delay_from_due_date has 545 (1.2%) zerosZeros
Num_Credit_Inquiries has 3021 (6.9%) zerosZeros
Total_EMI_per_month has 4603 (10.5%) zerosZeros

Reproduction

Analysis started2024-08-13 10:18:34.167539
Analysis finished2024-08-13 10:19:08.738354
Duration34.57 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Occupation
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size342.5 KiB
_______
3033 
Architect
 
2888
Lawyer
 
2835
Mechanic
 
2808
Scientist
 
2805
Other values (11)
29449 

Length

Max length13
Median length10
Mean length8.4188461
Min length6

Characters and Unicode

Total characters368897
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScientist
2nd rowScientist
3rd rowScientist
4th rowScientist
5th rowScientist

Common Values

ValueCountFrequency (%)
_______ 3033
 
6.9%
Architect 2888
 
6.6%
Lawyer 2835
 
6.5%
Mechanic 2808
 
6.4%
Scientist 2805
 
6.4%
Teacher 2795
 
6.4%
Media_Manager 2746
 
6.3%
Developer 2719
 
6.2%
Entrepreneur 2706
 
6.2%
Doctor 2701
 
6.2%
Other values (6) 15782
36.0%

Length

2024-08-13T10:19:09.010695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3033
 
6.9%
architect 2888
 
6.6%
lawyer 2835
 
6.5%
mechanic 2808
 
6.4%
scientist 2805
 
6.4%
teacher 2795
 
6.4%
media_manager 2746
 
6.3%
developer 2719
 
6.2%
entrepreneur 2706
 
6.2%
doctor 2701
 
6.2%
Other values (6) 15782
36.0%

Most occurring characters

ValueCountFrequency (%)
e 49395
13.4%
r 38128
10.3%
n 32062
 
8.7%
a 29713
 
8.1%
i 27366
 
7.4%
c 27309
 
7.4%
t 27086
 
7.3%
_ 23977
 
6.5%
M 13575
 
3.7%
o 13254
 
3.6%
Other values (18) 87032
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 368897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 49395
13.4%
r 38128
10.3%
n 32062
 
8.7%
a 29713
 
8.1%
i 27366
 
7.4%
c 27309
 
7.4%
t 27086
 
7.3%
_ 23977
 
6.5%
M 13575
 
3.7%
o 13254
 
3.6%
Other values (18) 87032
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 368897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 49395
13.4%
r 38128
10.3%
n 32062
 
8.7%
a 29713
 
8.1%
i 27366
 
7.4%
c 27309
 
7.4%
t 27086
 
7.3%
_ 23977
 
6.5%
M 13575
 
3.7%
o 13254
 
3.6%
Other values (18) 87032
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 368897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 49395
13.4%
r 38128
10.3%
n 32062
 
8.7%
a 29713
 
8.1%
i 27366
 
7.4%
c 27309
 
7.4%
t 27086
 
7.3%
_ 23977
 
6.5%
M 13575
 
3.7%
o 13254
 
3.6%
Other values (18) 87032
23.6%
Distinct8248
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size342.5 KiB
2024-08-13T10:19:09.635682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length8
Mean length8.2952896
Min length6

Characters and Unicode

Total characters363483
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2231 ?
Unique (%)5.1%

Sample

1st row19114.12
2nd row19114.12
3rd row19114.12
4th row19114.12
5th row19114.12
ValueCountFrequency (%)
109945.32 16
 
< 0.1%
40341.16 16
 
< 0.1%
38635.95 8
 
< 0.1%
116336.32 8
 
< 0.1%
62168.04 8
 
< 0.1%
28169.07 8
 
< 0.1%
67583.2 8
 
< 0.1%
170751.96 8
 
< 0.1%
62223.06 8
 
< 0.1%
15982.36 8
 
< 0.1%
Other values (5890) 43722
99.8%
2024-08-13T10:19:10.619875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 43818
12.1%
1 40845
11.2%
2 33479
9.2%
4 31891
8.8%
8 31392
8.6%
5 31028
8.5%
3 30979
8.5%
6 30779
8.5%
9 29919
8.2%
0 28739
7.9%
Other values (2) 30614
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 363483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 43818
12.1%
1 40845
11.2%
2 33479
9.2%
4 31891
8.8%
8 31392
8.6%
5 31028
8.5%
3 30979
8.5%
6 30779
8.5%
9 29919
8.2%
0 28739
7.9%
Other values (2) 30614
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 363483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 43818
12.1%
1 40845
11.2%
2 33479
9.2%
4 31891
8.8%
8 31392
8.6%
5 31028
8.5%
3 30979
8.5%
6 30779
8.5%
9 29919
8.2%
0 28739
7.9%
Other values (2) 30614
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 363483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 43818
12.1%
1 40845
11.2%
2 33479
9.2%
4 31891
8.8%
8 31392
8.6%
5 31028
8.5%
3 30979
8.5%
6 30779
8.5%
9 29919
8.2%
0 28739
7.9%
Other values (2) 30614
8.4%

Monthly_Inhand_Salary
Real number (ℝ)

MISSING 

Distinct5792
Distinct (%)15.5%
Missing6548
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean4215.7387
Minimum319.55625
Maximum15136.697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2024-08-13T10:19:10.949596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum319.55625
5-th percentile840.62458
Q11637.925
median3091
Q35989.7317
95-th percentile10899.153
Maximum15136.697
Range14817.14
Interquartile range (IQR)4351.8067

Descriptive statistics

Standard deviation3209.5493
Coefficient of variation (CV)0.76132548
Kurtosis0.6443932
Mean4215.7387
Median Absolute Deviation (MAD)1749.0117
Skewness1.1382554
Sum1.5712058 × 108
Variance10301207
MonotonicityNot monotonic
2024-08-13T10:19:11.234764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6082.1875 15
 
< 0.1%
3080.555 14
 
< 0.1%
4387.2725 13
 
< 0.1%
1720.684167 8
 
< 0.1%
2035.149167 8
 
< 0.1%
4751.89 8
 
< 0.1%
1033.626667 8
 
< 0.1%
8299.705 8
 
< 0.1%
13599 8
 
< 0.1%
958.8625 8
 
< 0.1%
Other values (5782) 37172
84.8%
(Missing) 6548
 
14.9%
ValueCountFrequency (%)
319.55625 7
< 0.1%
332.1283333 7
< 0.1%
333.5966667 6
< 0.1%
355.2083333 8
< 0.1%
357.2558333 7
< 0.1%
361.6033333 6
< 0.1%
368.3741667 7
< 0.1%
379.3908333 6
< 0.1%
380.6491667 6
< 0.1%
391.0533333 8
< 0.1%
ValueCountFrequency (%)
15136.69667 7
< 0.1%
14978.33667 7
< 0.1%
14958.33667 3
 
< 0.1%
14866.44667 8
< 0.1%
14862.28333 5
< 0.1%
14855.93 6
< 0.1%
14839.7 6
< 0.1%
14836.73667 6
< 0.1%
14828.98333 8
< 0.1%
14816.94 8
< 0.1%

Num_Bank_Accounts
Real number (ℝ)

ZEROS 

Distinct490
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.882628
Minimum-1
Maximum1798
Zeros2005
Zeros (%)4.6%
Negative13
Negative (%)< 0.1%
Memory size342.5 KiB
2024-08-13T10:19:11.531050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median6
Q37
95-th percentile10
Maximum1798
Range1799
Interquartile range (IQR)4

Descriptive statistics

Standard deviation116.96931
Coefficient of variation (CV)6.9283827
Kurtosis135.90916
Mean16.882628
Median Absolute Deviation (MAD)2
Skewness11.339162
Sum739763
Variance13681.819
MonotonicityNot monotonic
2024-08-13T10:19:11.812707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 5743
13.1%
8 5734
13.1%
7 5609
12.8%
5 5213
11.9%
3 5208
11.9%
4 5082
11.6%
9 2391
5.5%
10 2211
 
5.0%
1 2097
 
4.8%
0 2005
 
4.6%
Other values (480) 2525
5.8%
ValueCountFrequency (%)
-1 13
 
< 0.1%
0 2005
 
4.6%
1 2097
 
4.8%
2 1955
 
4.5%
3 5208
11.9%
4 5082
11.6%
5 5213
11.9%
6 5743
13.1%
7 5609
12.8%
8 5734
13.1%
ValueCountFrequency (%)
1798 1
< 0.1%
1794 1
< 0.1%
1793 1
< 0.1%
1789 1
< 0.1%
1784 1
< 0.1%
1783 1
< 0.1%
1779 2
< 0.1%
1777 1
< 0.1%
1771 1
< 0.1%
1769 1
< 0.1%

Num_Credit_Card
Real number (ℝ)

Distinct746
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.201059
Minimum0
Maximum1499
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2024-08-13T10:19:12.104746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median6
Q37
95-th percentile10
Maximum1499
Range1499
Interquartile range (IQR)3

Descriptive statistics

Standard deviation132.31365
Coefficient of variation (CV)5.7029143
Kurtosis70.972233
Mean23.201059
Median Absolute Deviation (MAD)1
Skewness8.2707188
Sum1016624
Variance17506.902
MonotonicityNot monotonic
2024-08-13T10:19:12.379009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 7944
18.1%
7 7654
17.5%
6 7263
16.6%
4 5880
13.4%
3 5819
13.3%
8 2204
 
5.0%
9 2114
 
4.8%
10 1952
 
4.5%
2 1014
 
2.3%
1 937
 
2.1%
Other values (736) 1037
 
2.4%
ValueCountFrequency (%)
0 13
 
< 0.1%
1 937
 
2.1%
2 1014
 
2.3%
3 5819
13.3%
4 5880
13.4%
5 7944
18.1%
6 7263
16.6%
7 7654
17.5%
8 2204
 
5.0%
9 2114
 
4.8%
ValueCountFrequency (%)
1499 1
< 0.1%
1498 1
< 0.1%
1497 2
< 0.1%
1494 1
< 0.1%
1493 1
< 0.1%
1486 1
< 0.1%
1485 1
< 0.1%
1480 2
< 0.1%
1479 2
< 0.1%
1477 1
< 0.1%

Interest_Rate
Real number (ℝ)

Distinct861
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.922178
Minimum1
Maximum5788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2024-08-13T10:19:12.683222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median13
Q320
95-th percentile33
Maximum5788
Range5787
Interquartile range (IQR)12

Descriptive statistics

Standard deviation459.98063
Coefficient of variation (CV)6.4857093
Kurtosis87.957873
Mean70.922178
Median Absolute Deviation (MAD)6
Skewness9.1484617
Sum3107668
Variance211582.18
MonotonicityNot monotonic
2024-08-13T10:19:12.960882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 2267
 
5.2%
5 2160
 
4.9%
6 2048
 
4.7%
12 2042
 
4.7%
11 1988
 
4.5%
7 1974
 
4.5%
9 1913
 
4.4%
10 1884
 
4.3%
18 1759
 
4.0%
15 1689
 
3.9%
Other values (851) 24094
55.0%
ValueCountFrequency (%)
1 1182
2.7%
2 1129
2.6%
3 1231
2.8%
4 1228
2.8%
5 2160
4.9%
6 2048
4.7%
7 1974
4.5%
8 2267
5.2%
9 1913
4.4%
10 1884
4.3%
ValueCountFrequency (%)
5788 1
< 0.1%
5773 1
< 0.1%
5763 1
< 0.1%
5762 1
< 0.1%
5756 1
< 0.1%
5747 1
< 0.1%
5745 1
< 0.1%
5743 1
< 0.1%
5739 1
< 0.1%
5722 1
< 0.1%
Distinct220
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size342.5 KiB
2024-08-13T10:19:13.555767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.1719841
Min length1

Characters and Unicode

Total characters51354
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)0.4%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4
ValueCountFrequency (%)
2 6915
15.8%
4 6669
15.2%
3 6631
15.1%
0 4744
10.8%
1 4215
9.6%
6 3501
8.0%
7 3219
7.3%
5 3044
6.9%
100 1649
 
3.8%
9 1624
 
3.7%
Other values (198) 1607
 
3.7%
2024-08-13T10:19:14.480283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8090
15.8%
2 6977
13.6%
4 6756
13.2%
3 6711
13.1%
1 6012
11.7%
6 3550
6.9%
7 3259
6.3%
5 3102
 
6.0%
_ 2119
 
4.1%
9 1681
 
3.3%
Other values (2) 3097
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8090
15.8%
2 6977
13.6%
4 6756
13.2%
3 6711
13.1%
1 6012
11.7%
6 3550
6.9%
7 3259
6.3%
5 3102
 
6.0%
_ 2119
 
4.1%
9 1681
 
3.3%
Other values (2) 3097
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8090
15.8%
2 6977
13.6%
4 6756
13.2%
3 6711
13.1%
1 6012
11.7%
6 3550
6.9%
7 3259
6.3%
5 3102
 
6.0%
_ 2119
 
4.1%
9 1681
 
3.3%
Other values (2) 3097
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8090
15.8%
2 6977
13.6%
4 6756
13.2%
3 6711
13.1%
1 6012
11.7%
6 3550
6.9%
7 3259
6.3%
5 3102
 
6.0%
_ 2119
 
4.1%
9 1681
 
3.3%
Other values (2) 3097
 
6.0%

Type_of_Loan
Text

MISSING 

Distinct3099
Distinct (%)8.0%
Missing4946
Missing (%)11.3%
Memory size342.5 KiB
2024-08-13T10:19:14.790595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length178
Median length138
Mean length66.981889
Min length9

Characters and Unicode

Total characters2603720
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
2nd rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
3rd rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
4th rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
5th rowAuto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan
ValueCountFrequency (%)
loan 138472
36.5%
and 34440
 
9.1%
payday 18304
 
4.8%
credit-builder 17984
 
4.7%
student 17424
 
4.6%
personal 17248
 
4.5%
not 17224
 
4.5%
specified 17224
 
4.5%
mortgage 17056
 
4.5%
debt 17024
 
4.5%
Other values (4) 67296
17.7%
2024-08-13T10:19:15.406356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
340824
13.1%
o 274504
10.5%
a 260848
 
10.0%
n 241632
 
9.3%
e 156008
 
6.0%
t 154592
 
5.9%
d 140384
 
5.4%
L 138472
 
5.3%
i 121304
 
4.7%
, 116824
 
4.5%
Other values (23) 658328
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2603720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
340824
13.1%
o 274504
10.5%
a 260848
 
10.0%
n 241632
 
9.3%
e 156008
 
6.0%
t 154592
 
5.9%
d 140384
 
5.4%
L 138472
 
5.3%
i 121304
 
4.7%
, 116824
 
4.5%
Other values (23) 658328
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2603720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
340824
13.1%
o 274504
10.5%
a 260848
 
10.0%
n 241632
 
9.3%
e 156008
 
6.0%
t 154592
 
5.9%
d 140384
 
5.4%
L 138472
 
5.3%
i 121304
 
4.7%
, 116824
 
4.5%
Other values (23) 658328
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2603720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
340824
13.1%
o 274504
10.5%
a 260848
 
10.0%
n 241632
 
9.3%
e 156008
 
6.0%
t 154592
 
5.9%
d 140384
 
5.4%
L 138472
 
5.3%
i 121304
 
4.7%
, 116824
 
4.5%
Other values (23) 658328
25.3%

Delay_from_due_date
Real number (ℝ)

ZEROS 

Distinct73
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.13814
Minimum-5
Maximum67
Zeros545
Zeros (%)1.2%
Negative253
Negative (%)0.6%
Memory size342.5 KiB
2024-08-13T10:19:15.704738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile3
Q110
median18
Q328
95-th percentile54
Maximum67
Range72
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.908357
Coefficient of variation (CV)0.70528234
Kurtosis0.32419292
Mean21.13814
Median Absolute Deviation (MAD)9
Skewness0.95933265
Sum926231
Variance222.2591
MonotonicityNot monotonic
2024-08-13T10:19:15.998758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 1493
 
3.4%
15 1488
 
3.4%
14 1477
 
3.4%
12 1465
 
3.3%
10 1427
 
3.3%
8 1421
 
3.2%
5 1383
 
3.2%
11 1379
 
3.1%
9 1369
 
3.1%
6 1347
 
3.1%
Other values (63) 29569
67.5%
ValueCountFrequency (%)
-5 15
 
< 0.1%
-4 22
 
0.1%
-3 52
 
0.1%
-2 73
 
0.2%
-1 91
 
0.2%
0 545
1.2%
1 577
1.3%
2 612
1.4%
3 752
1.7%
4 733
1.7%
ValueCountFrequency (%)
67 11
 
< 0.1%
66 16
 
< 0.1%
65 24
 
0.1%
64 30
 
0.1%
63 32
 
0.1%
62 233
0.5%
61 240
0.5%
60 210
0.5%
59 223
0.5%
58 212
0.5%
Distinct350
Distinct (%)0.9%
Missing3085
Missing (%)7.0%
Memory size342.5 KiB
2024-08-13T10:19:16.350483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length2
Mean length1.7652272
Min length1

Characters and Unicode

Total characters71903
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique275 ?
Unique (%)0.7%

Sample

1st row7
2nd row7
3rd row4
4th row4
5th row8_
ValueCountFrequency (%)
19 2467
 
6.1%
17 2413
 
5.9%
16 2349
 
5.8%
15 2291
 
5.6%
18 2288
 
5.6%
20 2284
 
5.6%
8 2146
 
5.3%
12 2141
 
5.3%
9 2132
 
5.2%
10 2124
 
5.2%
Other values (305) 18098
44.4%
2024-08-13T10:19:16.982455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 26030
36.2%
2 11395
15.8%
0 5217
 
7.3%
9 4685
 
6.5%
8 4588
 
6.4%
5 3974
 
5.5%
3 3845
 
5.3%
7 3634
 
5.1%
6 3562
 
5.0%
4 3486
 
4.8%
Other values (2) 1487
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 26030
36.2%
2 11395
15.8%
0 5217
 
7.3%
9 4685
 
6.5%
8 4588
 
6.4%
5 3974
 
5.5%
3 3845
 
5.3%
7 3634
 
5.1%
6 3562
 
5.0%
4 3486
 
4.8%
Other values (2) 1487
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 26030
36.2%
2 11395
15.8%
0 5217
 
7.3%
9 4685
 
6.5%
8 4588
 
6.4%
5 3974
 
5.5%
3 3845
 
5.3%
7 3634
 
5.1%
6 3562
 
5.0%
4 3486
 
4.8%
Other values (2) 1487
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 26030
36.2%
2 11395
15.8%
0 5217
 
7.3%
9 4685
 
6.5%
8 4588
 
6.4%
5 3974
 
5.5%
3 3845
 
5.3%
7 3634
 
5.1%
6 3562
 
5.0%
4 3486
 
4.8%
Other values (2) 1487
 
2.1%
Distinct3698
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size342.5 KiB
2024-08-13T10:19:17.537169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length20
Mean length4.7201607
Min length1

Characters and Unicode

Total characters206828
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique843 ?
Unique (%)1.9%

Sample

1st row11.27
2nd row11.27
3rd row_
4th row6.27
5th row11.27
ValueCountFrequency (%)
898
 
2.0%
11.32 76
 
0.2%
11.73 75
 
0.2%
7.01 68
 
0.2%
10.54 67
 
0.2%
9.39 67
 
0.2%
9.5 62
 
0.1%
7.33 61
 
0.1%
9.13 61
 
0.1%
4.53 60
 
0.1%
Other values (3297) 42323
96.6%
2024-08-13T10:19:18.406875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 42919
20.8%
1 29775
14.4%
9 20769
10.0%
0 17934
8.7%
2 15855
 
7.7%
7 13315
 
6.4%
8 13313
 
6.4%
5 13310
 
6.4%
6 12979
 
6.3%
3 12891
 
6.2%
Other values (3) 13768
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 206828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 42919
20.8%
1 29775
14.4%
9 20769
10.0%
0 17934
8.7%
2 15855
 
7.7%
7 13315
 
6.4%
8 13313
 
6.4%
5 13310
 
6.4%
6 12979
 
6.3%
3 12891
 
6.2%
Other values (3) 13768
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 206828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 42919
20.8%
1 29775
14.4%
9 20769
10.0%
0 17934
8.7%
2 15855
 
7.7%
7 13315
 
6.4%
8 13313
 
6.4%
5 13310
 
6.4%
6 12979
 
6.3%
3 12891
 
6.2%
Other values (3) 13768
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 206828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 42919
20.8%
1 29775
14.4%
9 20769
10.0%
0 17934
8.7%
2 15855
 
7.7%
7 13315
 
6.4%
8 13313
 
6.4%
5 13310
 
6.4%
6 12979
 
6.3%
3 12891
 
6.2%
Other values (3) 13768
 
6.7%

Num_Credit_Inquiries
Real number (ℝ)

MISSING  ZEROS 

Distinct629
Distinct (%)1.5%
Missing840
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean26.448718
Minimum0
Maximum2597
Zeros3021
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2024-08-13T10:19:18.729944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q39
95-th percentile13
Maximum2597
Range2597
Interquartile range (IQR)6

Descriptive statistics

Standard deviation184.53258
Coefficient of variation (CV)6.9769953
Kurtosis105.02193
Mean26.448718
Median Absolute Deviation (MAD)3
Skewness9.9774858
Sum1136713
Variance34052.273
MonotonicityNot monotonic
2024-08-13T10:19:19.041049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 5019
11.5%
3 3717
8.5%
2 3716
8.5%
6 3555
 
8.1%
8 3367
 
7.7%
7 3332
 
7.6%
1 3303
 
7.5%
0 3021
 
6.9%
5 2612
 
6.0%
9 2346
 
5.4%
Other values (619) 8990
20.5%
ValueCountFrequency (%)
0 3021
6.9%
1 3303
7.5%
2 3716
8.5%
3 3717
8.5%
4 5019
11.5%
5 2612
6.0%
6 3555
8.1%
7 3332
7.6%
8 3367
7.7%
9 2346
5.4%
ValueCountFrequency (%)
2597 1
< 0.1%
2594 1
< 0.1%
2592 1
< 0.1%
2588 1
< 0.1%
2587 1
< 0.1%
2580 1
< 0.1%
2573 1
< 0.1%
2572 1
< 0.1%
2568 1
< 0.1%
2564 1
< 0.1%

Credit_Mix
Categorical

Distinct4
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size342.5 KiB
Standard
15610 
Good
10881 
_
8907 
Bad
8419 

Length

Max length8
Median length4
Mean length4.6230458
Min length1

Characters and Unicode

Total characters202568
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row_
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Standard 15610
35.6%
Good 10881
24.8%
_ 8907
20.3%
Bad 8419
19.2%
(Missing) 1
 
< 0.1%

Length

2024-08-13T10:19:19.574336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T10:19:19.856078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
standard 15610
35.6%
good 10881
24.8%
8907
20.3%
bad 8419
19.2%

Most occurring characters

ValueCountFrequency (%)
d 50520
24.9%
a 39639
19.6%
o 21762
10.7%
S 15610
 
7.7%
t 15610
 
7.7%
n 15610
 
7.7%
r 15610
 
7.7%
G 10881
 
5.4%
_ 8907
 
4.4%
B 8419
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 50520
24.9%
a 39639
19.6%
o 21762
10.7%
S 15610
 
7.7%
t 15610
 
7.7%
n 15610
 
7.7%
r 15610
 
7.7%
G 10881
 
5.4%
_ 8907
 
4.4%
B 8419
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 50520
24.9%
a 39639
19.6%
o 21762
10.7%
S 15610
 
7.7%
t 15610
 
7.7%
n 15610
 
7.7%
r 15610
 
7.7%
G 10881
 
5.4%
_ 8907
 
4.4%
B 8419
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 50520
24.9%
a 39639
19.6%
o 21762
10.7%
S 15610
 
7.7%
t 15610
 
7.7%
n 15610
 
7.7%
r 15610
 
7.7%
G 10881
 
5.4%
_ 8907
 
4.4%
B 8419
 
4.2%
Distinct5837
Distinct (%)13.3%
Missing1
Missing (%)< 0.1%
Memory size342.5 KiB
2024-08-13T10:19:20.430551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.4258165
Min length3

Characters and Unicode

Total characters281560
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique417 ?
Unique (%)1.0%

Sample

1st row809.98
2nd row809.98
3rd row809.98
4th row809.98
5th row809.98
ValueCountFrequency (%)
143.04 16
 
< 0.1%
585.77 16
 
< 0.1%
1464.16 16
 
< 0.1%
177.03 16
 
< 0.1%
298.5 16
 
< 0.1%
729.74 16
 
< 0.1%
352.44 16
 
< 0.1%
573.77 16
 
< 0.1%
849.98 16
 
< 0.1%
2538.81 16
 
< 0.1%
Other values (5399) 43657
99.6%
2024-08-13T10:19:21.559992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 43817
15.6%
1 36505
13.0%
2 27553
9.8%
4 25889
9.2%
3 25522
9.1%
5 21552
7.7%
6 21280
7.6%
7 21217
7.5%
9 20936
7.4%
8 20816
7.4%
Other values (2) 16473
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 281560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 43817
15.6%
1 36505
13.0%
2 27553
9.8%
4 25889
9.2%
3 25522
9.1%
5 21552
7.7%
6 21280
7.6%
7 21217
7.5%
9 20936
7.4%
8 20816
7.4%
Other values (2) 16473
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 281560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 43817
15.6%
1 36505
13.0%
2 27553
9.8%
4 25889
9.2%
3 25522
9.1%
5 21552
7.7%
6 21280
7.6%
7 21217
7.5%
9 20936
7.4%
8 20816
7.4%
Other values (2) 16473
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 281560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 43817
15.6%
1 36505
13.0%
2 27553
9.8%
4 25889
9.2%
3 25522
9.1%
5 21552
7.7%
6 21280
7.6%
7 21217
7.5%
9 20936
7.4%
8 20816
7.4%
Other values (2) 16473
 
5.9%

Credit_Utilization_Ratio
Real number (ℝ)

Distinct43817
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean32.311347
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2024-08-13T10:19:22.069569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24.250152
Q128.06487
median32.328808
Q336.54394
95-th percentile40.253935
Maximum50
Range30
Interquartile range (IQR)8.4790701

Descriptive statistics

Standard deviation5.1281804
Coefficient of variation (CV)0.15871144
Kurtosis-0.9479879
Mean32.311347
Median Absolute Deviation (MAD)4.2395091
Skewness0.030353284
Sum1415786.3
Variance26.298234
MonotonicityNot monotonic
2024-08-13T10:19:22.494804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.82261962 1
 
< 0.1%
27.37715366 1
 
< 0.1%
34.34149209 1
 
< 0.1%
35.5973986 1
 
< 0.1%
28.9324999 1
 
< 0.1%
40.7471509 1
 
< 0.1%
29.81840602 1
 
< 0.1%
24.05834783 1
 
< 0.1%
37.34687182 1
 
< 0.1%
37.94858471 1
 
< 0.1%
Other values (43807) 43807
> 99.9%
ValueCountFrequency (%)
20 1
< 0.1%
20.1729419 1
< 0.1%
20.24413035 1
< 0.1%
20.71974515 1
< 0.1%
20.98560579 1
< 0.1%
20.98591888 1
< 0.1%
20.992914 1
< 0.1%
21.02766451 1
< 0.1%
21.0567212 1
< 0.1%
21.22850297 1
< 0.1%
ValueCountFrequency (%)
50 1
< 0.1%
49.5223243 1
< 0.1%
48.24700252 1
< 0.1%
47.96956024 1
< 0.1%
47.92766496 1
< 0.1%
47.64242451 1
< 0.1%
47.61224433 1
< 0.1%
47.5559826 1
< 0.1%
47.5370307 1
< 0.1%
47.29400692 1
< 0.1%

Credit_History_Age
Text

MISSING 

Distinct404
Distinct (%)1.0%
Missing3986
Missing (%)9.1%
Memory size342.5 KiB
2024-08-13T10:19:23.066073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length21
Mean length20.992569
Min length20

Characters and Unicode

Total characters836176
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22 Years and 1 Months
2nd row22 Years and 3 Months
3rd row22 Years and 4 Months
4th row22 Years and 5 Months
5th row22 Years and 6 Months
ValueCountFrequency (%)
and 39832
20.0%
months 39832
20.0%
years 39832
20.0%
11 4777
 
2.4%
9 4702
 
2.4%
8 4681
 
2.4%
10 4668
 
2.3%
5 4260
 
2.1%
6 4194
 
2.1%
7 4077
 
2.0%
Other values (27) 48305
24.3%
2024-08-13T10:19:23.912879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
159328
19.1%
a 79664
9.5%
s 79664
9.5%
n 79664
9.5%
M 39832
 
4.8%
o 39832
 
4.8%
Y 39832
 
4.8%
e 39832
 
4.8%
r 39832
 
4.8%
d 39832
 
4.8%
Other values (12) 198864
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 836176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
159328
19.1%
a 79664
9.5%
s 79664
9.5%
n 79664
9.5%
M 39832
 
4.8%
o 39832
 
4.8%
Y 39832
 
4.8%
e 39832
 
4.8%
r 39832
 
4.8%
d 39832
 
4.8%
Other values (12) 198864
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 836176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
159328
19.1%
a 79664
9.5%
s 79664
9.5%
n 79664
9.5%
M 39832
 
4.8%
o 39832
 
4.8%
Y 39832
 
4.8%
e 39832
 
4.8%
r 39832
 
4.8%
d 39832
 
4.8%
Other values (12) 198864
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 836176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
159328
19.1%
a 79664
9.5%
s 79664
9.5%
n 79664
9.5%
M 39832
 
4.8%
o 39832
 
4.8%
Y 39832
 
4.8%
e 39832
 
4.8%
r 39832
 
4.8%
d 39832
 
4.8%
Other values (12) 198864
23.8%
Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size342.5 KiB
Yes
22964 
No
15591 
NM
5262 

Length

Max length3
Median length3
Mean length2.5240888
Min length2

Characters and Unicode

Total characters110598
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Yes 22964
52.4%
No 15591
35.6%
NM 5262
 
12.0%
(Missing) 1
 
< 0.1%

Length

2024-08-13T10:19:24.215335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T10:19:24.465427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
yes 22964
52.4%
no 15591
35.6%
nm 5262
 
12.0%

Most occurring characters

ValueCountFrequency (%)
Y 22964
20.8%
e 22964
20.8%
s 22964
20.8%
N 20853
18.9%
o 15591
14.1%
M 5262
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 22964
20.8%
e 22964
20.8%
s 22964
20.8%
N 20853
18.9%
o 15591
14.1%
M 5262
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 22964
20.8%
e 22964
20.8%
s 22964
20.8%
N 20853
18.9%
o 15591
14.1%
M 5262
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 22964
20.8%
e 22964
20.8%
s 22964
20.8%
N 20853
18.9%
o 15591
14.1%
M 5262
 
4.8%

Total_EMI_per_month
Real number (ℝ)

ZEROS 

Distinct6548
Distinct (%)14.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1388.3316
Minimum0
Maximum82256
Zeros4603
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size342.5 KiB
2024-08-13T10:19:24.708873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130.907553
median69.692591
Q3162.34322
95-th percentile445.55592
Maximum82256
Range82256
Interquartile range (IQR)131.43567

Descriptive statistics

Standard deviation8281.3157
Coefficient of variation (CV)5.9649406
Kurtosis53.556702
Mean1388.3316
Median Absolute Deviation (MAD)49.983938
Skewness7.1888797
Sum60832527
Variance68580190
MonotonicityNot monotonic
2024-08-13T10:19:24.979074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4603
 
10.5%
49.57494921 8
 
< 0.1%
67.30924983 8
 
< 0.1%
19.79281109 8
 
< 0.1%
56.52993708 8
 
< 0.1%
124.9486672 8
 
< 0.1%
13.79706265 8
 
< 0.1%
16.78352528 8
 
< 0.1%
38.45595247 8
 
< 0.1%
144.6297138 8
 
< 0.1%
Other values (6538) 39142
89.3%
ValueCountFrequency (%)
0 4603
10.5%
4.462837467 8
 
< 0.1%
4.865689677 8
 
< 0.1%
4.916138542 8
 
< 0.1%
5.138484696 8
 
< 0.1%
5.218466359 8
 
< 0.1%
5.463308978 7
 
< 0.1%
5.629824417 8
 
< 0.1%
5.711416879 8
 
< 0.1%
5.905518076 8
 
< 0.1%
ValueCountFrequency (%)
82256 1
< 0.1%
82236 1
< 0.1%
82204 1
< 0.1%
82178 1
< 0.1%
81902 1
< 0.1%
81730 1
< 0.1%
81578 1
< 0.1%
81553 1
< 0.1%
81441 1
< 0.1%
81301 1
< 0.1%
Distinct39960
Distinct (%)95.4%
Missing1940
Missing (%)4.4%
Memory size342.5 KiB
2024-08-13T10:19:25.384947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length17
Mean length16.973924
Min length3

Characters and Unicode

Total characters710834
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39958 ?
Unique (%)95.4%

Sample

1st row80.41529543900253
2nd row118.28022162236736
3rd row81.699521264648
4th row199.4580743910713
5th row41.420153086217326
ValueCountFrequency (%)
10000 1846
 
4.4%
0.0 74
 
0.2%
118.28022162236736 1
 
< 0.1%
24.785216509052056 1
 
< 0.1%
825.2162699393922 1
 
< 0.1%
232.86038375993544 1
 
< 0.1%
81.699521264648 1
 
< 0.1%
199.4580743910713 1
 
< 0.1%
41.420153086217326 1
 
< 0.1%
62.430172331195294 1
 
< 0.1%
Other values (39950) 39950
95.4%
2024-08-13T10:19:26.089220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 75982
10.7%
2 69101
9.7%
4 66147
9.3%
3 66127
9.3%
0 65886
9.3%
5 65823
9.3%
6 65259
9.2%
8 63966
9.0%
7 63627
9.0%
9 61500
8.7%
Other values (2) 47416
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 710834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 75982
10.7%
2 69101
9.7%
4 66147
9.3%
3 66127
9.3%
0 65886
9.3%
5 65823
9.3%
6 65259
9.2%
8 63966
9.0%
7 63627
9.0%
9 61500
8.7%
Other values (2) 47416
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 710834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 75982
10.7%
2 69101
9.7%
4 66147
9.3%
3 66127
9.3%
0 65886
9.3%
5 65823
9.3%
6 65259
9.2%
8 63966
9.0%
7 63627
9.0%
9 61500
8.7%
Other values (2) 47416
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 710834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 75982
10.7%
2 69101
9.7%
4 66147
9.3%
3 66127
9.3%
0 65886
9.3%
5 65823
9.3%
6 65259
9.2%
8 63966
9.0%
7 63627
9.0%
9 61500
8.7%
Other values (2) 47416
6.7%
Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size342.5 KiB
Low_spent_Small_value_payments
11217 
High_spent_Medium_value_payments
7707 
Low_spent_Medium_value_payments
6091 
High_spent_Large_value_payments
5903 
High_spent_Small_value_payments
5038 
Other values (2)
7861 

Length

Max length32
Median length31
Mean length28.920921
Min length6

Characters and Unicode

Total characters1267228
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh_spent_Small_value_payments
2nd rowLow_spent_Large_value_payments
3rd rowLow_spent_Medium_value_payments
4th rowLow_spent_Small_value_payments
5th rowHigh_spent_Medium_value_payments

Common Values

ValueCountFrequency (%)
Low_spent_Small_value_payments 11217
25.6%
High_spent_Medium_value_payments 7707
17.6%
Low_spent_Medium_value_payments 6091
13.9%
High_spent_Large_value_payments 5903
13.5%
High_spent_Small_value_payments 5038
11.5%
Low_spent_Large_value_payments 4539
10.4%
!@9#%8 3322
 
7.6%
(Missing) 1
 
< 0.1%

Length

2024-08-13T10:19:26.391081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T10:19:26.675481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
low_spent_small_value_payments 11217
25.6%
high_spent_medium_value_payments 7707
17.6%
low_spent_medium_value_payments 6091
13.9%
high_spent_large_value_payments 5903
13.5%
high_spent_small_value_payments 5038
11.5%
low_spent_large_value_payments 4539
10.4%
9#%8 3322
 
7.6%

Most occurring characters

ValueCountFrequency (%)
_ 161980
12.8%
e 145725
11.5%
a 107687
 
8.5%
s 80990
 
6.4%
p 80990
 
6.4%
n 80990
 
6.4%
t 80990
 
6.4%
l 73005
 
5.8%
m 70548
 
5.6%
u 54293
 
4.3%
Other values (19) 330030
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1267228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 161980
12.8%
e 145725
11.5%
a 107687
 
8.5%
s 80990
 
6.4%
p 80990
 
6.4%
n 80990
 
6.4%
t 80990
 
6.4%
l 73005
 
5.8%
m 70548
 
5.6%
u 54293
 
4.3%
Other values (19) 330030
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1267228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 161980
12.8%
e 145725
11.5%
a 107687
 
8.5%
s 80990
 
6.4%
p 80990
 
6.4%
n 80990
 
6.4%
t 80990
 
6.4%
l 73005
 
5.8%
m 70548
 
5.6%
u 54293
 
4.3%
Other values (19) 330030
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1267228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 161980
12.8%
e 145725
11.5%
a 107687
 
8.5%
s 80990
 
6.4%
p 80990
 
6.4%
n 80990
 
6.4%
t 80990
 
6.4%
l 73005
 
5.8%
m 70548
 
5.6%
u 54293
 
4.3%
Other values (19) 330030
26.0%

Monthly_Balance
Text

MISSING 

Distinct43251
Distinct (%)> 99.9%
Missing563
Missing (%)1.3%
Memory size342.5 KiB
2024-08-13T10:19:27.220044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length17
Mean length17.342804
Min length12

Characters and Unicode

Total characters750163
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43250 ?
Unique (%)> 99.9%

Sample

1st row312.49408867943663
2nd row284.62916249607184
3rd row331.2098628537912
4th row223.45130972736786
5th row341.48923103222177
ValueCountFrequency (%)
333333333333333333333333333 5
 
< 0.1%
358.12416760938714 1
 
< 0.1%
810.7821526659284 1
 
< 0.1%
705.931285531244 1
 
< 0.1%
426.5134106068658 1
 
< 0.1%
331.2098628537912 1
 
< 0.1%
223.45130972736786 1
 
< 0.1%
341.48923103222177 1
 
< 0.1%
340.4792117872438 1
 
< 0.1%
244.5653167062043 1
 
< 0.1%
Other values (43241) 43241
> 99.9%
2024-08-13T10:19:28.057723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 79359
10.6%
2 79022
10.5%
4 73932
9.9%
6 70688
9.4%
5 70672
9.4%
7 68818
9.2%
1 68768
9.2%
8 67893
9.1%
9 65116
8.7%
0 62620
8.3%
Other values (3) 43275
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750163
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 79359
10.6%
2 79022
10.5%
4 73932
9.9%
6 70688
9.4%
5 70672
9.4%
7 68818
9.2%
1 68768
9.2%
8 67893
9.1%
9 65116
8.7%
0 62620
8.3%
Other values (3) 43275
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750163
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 79359
10.6%
2 79022
10.5%
4 73932
9.9%
6 70688
9.4%
5 70672
9.4%
7 68818
9.2%
1 68768
9.2%
8 67893
9.1%
9 65116
8.7%
0 62620
8.3%
Other values (3) 43275
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750163
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 79359
10.6%
2 79022
10.5%
4 73932
9.9%
6 70688
9.4%
5 70672
9.4%
7 68818
9.2%
1 68768
9.2%
8 67893
9.1%
9 65116
8.7%
0 62620
8.3%
Other values (3) 43275
5.8%

Credit_Score
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size342.5 KiB
Standard
23017 
Poor
12939 
Good
7861 

Length

Max length8
Median length8
Mean length6.1011936
Min length4

Characters and Unicode

Total characters267336
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Standard 23017
52.5%
Poor 12939
29.5%
Good 7861
 
17.9%
(Missing) 1
 
< 0.1%

Length

2024-08-13T10:19:28.377090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T10:19:28.662160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
standard 23017
52.5%
poor 12939
29.5%
good 7861
 
17.9%

Most occurring characters

ValueCountFrequency (%)
d 53895
20.2%
a 46034
17.2%
o 41600
15.6%
r 35956
13.4%
S 23017
8.6%
t 23017
8.6%
n 23017
8.6%
P 12939
 
4.8%
G 7861
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 267336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 53895
20.2%
a 46034
17.2%
o 41600
15.6%
r 35956
13.4%
S 23017
8.6%
t 23017
8.6%
n 23017
8.6%
P 12939
 
4.8%
G 7861
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 267336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 53895
20.2%
a 46034
17.2%
o 41600
15.6%
r 35956
13.4%
S 23017
8.6%
t 23017
8.6%
n 23017
8.6%
P 12939
 
4.8%
G 7861
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 267336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 53895
20.2%
a 46034
17.2%
o 41600
15.6%
r 35956
13.4%
S 23017
8.6%
t 23017
8.6%
n 23017
8.6%
P 12939
 
4.8%
G 7861
 
2.9%

Interactions

2024-08-13T10:19:02.844234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:43.080087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:45.234293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:47.478288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:49.554557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:51.577885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:55.174688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:59.278987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:03.348701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:43.361058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:45.497385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:47.748452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:49.814257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:51.936393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:55.863064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:59.790983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:03.772122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:43.653114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:45.772369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:48.018230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:50.080636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:52.335469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:56.342614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:00.214016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:04.202746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:43.924639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:46.044662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:48.275414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:50.332850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:52.725766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:56.802700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:00.714803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:04.522115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:44.190670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:46.296700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:48.535283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:50.576698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:53.109977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:57.191769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:01.121776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:04.776214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:44.452676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:46.555518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:48.807531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:50.840929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:53.495630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:57.708747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:01.543149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:05.025034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:44.704439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:46.975266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:49.063054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:51.092310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:53.780934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:58.241554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:01.991018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:05.270750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:44.986870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:47.233797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:49.310139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:51.343396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:54.501860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:18:58.822841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T10:19:02.534099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2024-08-13T10:19:05.696841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-13T10:19:06.697560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-13T10:19:08.159518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_BalanceCredit_Score
0Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan3711.274.0_809.9826.82262022 Years and 1 MonthsNo49.57494980.41529543900253High_spent_Small_value_payments312.49408867943663Good
1Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan-1NaN11.274.0Good809.9831.944960NaNNo49.574949118.28022162236736Low_spent_Large_value_payments284.62916249607184Good
2Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan37_4.0Good809.9828.60935222 Years and 3 MonthsNo49.57494981.699521264648Low_spent_Medium_value_payments331.2098628537912Good
3Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan546.274.0Good809.9831.37786222 Years and 4 MonthsNo49.574949199.4580743910713Low_spent_Small_value_payments223.45130972736786Good
4Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan6NaN11.274.0Good809.9824.79734722 Years and 5 MonthsNo49.57494941.420153086217326High_spent_Medium_value_payments341.48923103222177Good
5Scientist19114.12NaN3434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan849.274.0Good809.9827.26225922 Years and 6 MonthsNo49.57494962.430172331195294!@9#%8340.4792117872438Good
6Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan38_11.274.0Good809.9822.53759322 Years and 7 MonthsNo49.574949178.3440674122349Low_spent_Small_value_payments244.5653167062043Good
7Scientist19114.121824.8433333434Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan3611.274.0Good809.9823.933795NaNNo49.57494924.785216509052056High_spent_Medium_value_payments358.12416760938714Standard
8_______34847.843037.9866672461Credit-Builder Loan345.422.0Good605.0324.46403126 Years and 7 MonthsNo18.816215104.291825168246Low_spent_Small_value_payments470.69062692529184Standard
9Teacher34847.843037.9866672461Credit-Builder Loan717.422.0Good605.0338.55084826 Years and 8 MonthsNo18.81621540.39123782853101High_spent_Large_value_payments484.5912142650067Good
OccupationAnnual_IncomeMonthly_Inhand_SalaryNum_Bank_AccountsNum_Credit_CardInterest_RateNum_of_LoanType_of_LoanDelay_from_due_dateNum_of_Delayed_PaymentChanged_Credit_LimitNum_Credit_InquiriesCredit_MixOutstanding_DebtCredit_Utilization_RatioCredit_History_AgePayment_of_Min_AmountTotal_EMI_per_monthAmount_invested_monthlyPayment_BehaviourMonthly_BalanceCredit_Score
43808Media_Manager20889.641616.8033331010226Credit-Builder Loan, Debt Consolidation Loan, Not Specified, Credit-Builder Loan, Credit-Builder Loan, and Home Equity Loan26NaN4.4210.0Bad2578.236.30818611 Years and 4 MonthsYes103.13108543.50561885424431Low_spent_Medium_value_payments295.0436293522199Poor
43809Media_Manager20889.641616.8033331010226Credit-Builder Loan, Debt Consolidation Loan, Not Specified, Credit-Builder Loan, Credit-Builder Loan, and Home Equity Loan23224.4210.0_2578.236.81178611 Years and 5 MonthsYes103.131085NaNLow_spent_Small_value_payments154.09231580292825Poor
43810Media_Manager20889.641616.8033331010226Credit-Builder Loan, Debt Consolidation Loan, Not Specified, Credit-Builder Loan, Credit-Builder Loan, and Home Equity Loan25224.4210.0Bad2578.233.09631211 Years and 6 MonthsYes103.13108529.896436674912525High_spent_Large_value_payments268.6528115315517Poor
43811Media_Manager20889.641616.8033331010226Credit-Builder Loan, Debt Consolidation Loan, Not Specified, Credit-Builder Loan, Credit-Builder Loan, and Home Equity Loan262210.4210.0Bad2578.232.48574211 Years and 7 MonthsNM103.13108557.72549621138061Low_spent_Medium_value_payments280.82375199508357Poor
43812Media_Manager20889.641616.8033331010226Credit-Builder Loan, Debt Consolidation Loan, Not Specified, Credit-Builder Loan, Credit-Builder Loan, and Home Equity Loan26224.4210.0Bad2578.225.92821111 Years and 8 MonthsYes103.131085180.59658622727363Low_spent_Small_value_payments167.95266197919057Standard
43813Media_Manager20889.641616.8033331010226_Credit-Builder Loan, Debt Consolidation Loan, Not Specified, Credit-Builder Loan, Credit-Builder Loan, and Home Equity Loan30224.4213.0_2578.233.49957811 Years and 9 MonthsYes103.131085139.30282132784546Low_spent_Medium_value_payments199.24642687861873Poor
43814Media_Manager20889.641616.8033331010226Credit-Builder Loan, Debt Consolidation Loan, Not Specified, Credit-Builder Loan, Credit-Builder Loan, and Home Equity Loan2622-2.5813.0Bad2578.235.75269511 Years and 10 MonthsYes103.13108577.30809056548823Low_spent_Small_value_payments271.241157640976Poor
43815Media_Manager20889.641616.8033331010226Credit-Builder Loan, Debt Consolidation Loan, Not Specified, Credit-Builder Loan, Credit-Builder Loan, and Home Equity Loan22NaN4.4213.0_2578.235.37295311 Years and 11 MonthsYes103.131085108.19339878469236High_spent_Small_value_payments210.35584942177186Poor
43816Journalist13315.21307.60000085160NaN1012_14.842.0Standard1233.7438.29979217 Years and 8 MonthsNo0.00000023.81356273732316High_spent_Large_value_payments346.94643726267685Standard
43817Journalist13315.2_NaN85160NaN11141NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN